#acl All:read,write = Results for Exercise Sheet 10 (Naive Bayes) = '''Please read the instructions below, before adding something to the table!''' Add your row to the table below, following the examples already there. Column 2, 3, and 4 = running time of your program for reading the CSV file, training, and prediction, respectively, in seconds (with exactly one digit after the dot). Column 5 = percentage of your predictions that were correct. Column 5 = machine specification as usual (processor frequency with exactly one digit after the dot, amount of RAM as an integer, no secondary details about processor). Column 6 = programming language (Java or C++). Column 7 = A short description of the feature selection improvements you made (if you made any). ||'''Name''' ||'''Reading time''' ||'''Training time''' ||'''Prediction time''' ||'''Correct %''' ||'''Processor / RAM''' ||'''Language''' ||'''Feature Selection''' || ||Florian B ||6.5s ||0.1s ||5.6s ||49% ||Intel X5560 @ 2.8GHz / 36GB ||Java || || ||Florian B ||6.6s ||0.1s ||4.7s ||54% ||Intel X5560 @ 2.8GHz / 36GB ||Java ||no stopwords || ||Florian B ||6.5s ||0.1s ||4.5s ||64% ||Intel X5560 @ 2.8GHz / 36GB ||Java ||no stopwords + also multi-class documents for training || ||Christoph S ||15.5s ||0.4s ||0.7s ||53% ||Core 2 Quad @ 2.8GHz / 4GB ||C++ || || ||Christoph S ||36.6s ||1.1s ||4.2s ||62% ||Core 2 Duo @ 1.6GHz / 2GB ||C++ ||no stopwords || ||Christoph S ||34.3s ||0.0s ||0.0s ||21% ||Core 2 Duo @ 1.6GHz / 2GB ||C++ ||Just predict "class 0" all the time ;) || ||Christoph S ||34.3s ||1.1s ||4.1s ||67% ||Core 2 Duo @ 1.6GHz / 2GB ||C++ ||No stopwords. 1653 training points. Used docs with multiple class labels (counting them). || ||Matthias H. ||24.8s ||0.4s ||1.7s ||42% ||Intel i3 @ 1.3GHz / 4GB ||Java || || ||Stefan ||10.0s ||0.1s ||43.0s ||47% ||Intel i5 @ 1.7GHz / 4GB ||Java || || ||Anthony ||7.0s ||6.0s ||23.0s ||18% ||Intel i7 @ 2.10GHz / 8GB ||Java || || ||Adrian B. ||3.5s ||0.1s ||0.1s ||47% ||Intel i7 @ 3.4GHz / 16GB ||C++ || || ||Adrian B. ||3.0s ||0.1s ||0.1s ||55% ||Intel i7 @ 3.4GHz / 16GB ||C++ ||no stopwords || ||Adrian B. ||3.0s ||0.1s ||0.1s ||67% ||Intel i7 @ 3.4GHz / 16GB ||C++ ||no stopwords. Use every tenth doc for training, no matter how many labels it has. || ||Aritz B ||7.7s ||0.1s ||5.4s ||26% ||Intel Core 2 Duo @ 2.4GHz / 6GB ||Java || || ||Tobias S ||23.9s ||1.1s ||46.9s ||22% ||Intel Atom @ 1.6GHz / 2GB ||C++ || || ||Chris Sch ||17.8s ||0.2s ||16.0s ||37% ||Core 2 Duo @ 2.5GHz / 4GB ||Java || || ||Simon S ||18.8s ||0.1s ||8.9s ||27% ||Intel X5560 @ 2.8GHz / 36GB ||C++ || || ||Ander B ||8.4s ||1.6s ||12.8s ||26% ||Intel Core i5 @ 2.3GHz / 8GB ||Java || || ||Ane R ||7.6s ||1.0s ||4.6s ||27% ||Intel i5-2450M @ 2.5GHz / 8GB ||Java || || ||Alves J ||7.9s ||4.2s ||17.1s ||29% ||Intel Core 2 Duo @ 2.4GHz / 4GB ||Java || || ||Tobias F ||10.3s ||3.4s ||24.8s ||19% ||Phenom II X4 965 @ 3.4GHz / 4GB ||Java || || ||Jan M ||21.3s ||0.3s ||1.5s ||54% ||Intel E5645 @ 2.4GHz / 23GB ||C++ || || ||Jan M ||18.2s ||0.3s ||1.2s ||62% ||Intel E5645 @ 2.4GHz / 23GB ||C++ ||no stopwords || ||Matthias F ||10.0s ||1.0s ||8.3.2s ||37% ||Intel i7 CPU @ 2.10GHz / 8GB ||C++ || || ||Andreas H ||3.8s ||0.0s ||1.9s ||50% ||Intel P8700 @ 2.53GHz / 4GB ||C++ ||word length >= 9 || ||Markus F ||8.6s ||0.0s ||5.2s ||54% ||Intel Core i5-2500K @ 3.30GHz / 8GB ||C++ ||no stopwords || ||Markus F ||8.6s ||0.0s ||10.7s ||75% ||Intel Core i5-2500K @ 3.30GHz / 8GB ||C++ ||no stopwords, 10% of all documents as training set || ||Stephanie E ||14.1s ||0.2s ||2.3s ||48% ||Intel Core i5 @ 2.3GHz / 8 GB ||Java || || ||Christoph G ||23.2s ||0.3s ||14.1s ||59% ||Intel Pentium D @ 2.8GHz / 2 GB ||C++ || || ||Bettina H ||10.1s ||0.1s ||5.5s ||49% ||Intel i3-2100 @ 3.10 GHz / 6GB ||Java || || ||Marc P||2.8s||1.5s||3.2s||50%||Intel i5-M480 @ 2.70GHz / 4GB||Java||word length >= 9|| ||Fabian S||4.9s||0.2s||15.7s||45.2%||Core 2 Duo @ 2.26GHz / 8GB||C++||word length >= 9||